James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies – business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

According to recent research from Accenture, nearly half (40 percent) of major corporate decisions are based on the good 'ole gut.

Interesting. But why?

61 percent said it was because good data was not available, and just over half (55 percent) said their decisions relied on qualitative and subjective factors.

More interesting. Of course it could easily be argued that even bad data is better than nothing, especially if you can make some assessment of how bad it might be or at least understand its limitations. And the second one should worry CEOs and boards across the country - "qualitative and subjective factors". These are often illegal - think insurance or banking where decisions about pricing or risk may not be based on these kind of factors - and always influenced by the underlying biases of the decision maker.But then we get to what I makes this interesting to me (as a writer on decision management):

Other reasons related to workforce challenges: 23 percent of
respondents said "insufficient quantitative skills in employees" were a
main impediment at their company, and 36 percent said their company
"faces a shortage of analytical talent."

These two are, frankly, only a problem if you think data is for helping an individual make a decision (decision support) and nothing else. If, as I do, you believe that data can also be used to automate and manage decisions (decision management) then these problems fade into the background.If the system tells the user what decision to make or even what 2 or 3 choices are valid, appropriate, legal and potentially profitable then the user does not need quantitative skills. The user just needs to be able to read and then use information. Call center representative should not be required to have quantitative skills to use customer data to make better retention decisions - they should be required to have people skills to make the customer feel good about the targeted retention offer the system suggests (that is based on policies, regulations and analytics). Using data to build decision management systems means that the users don't need to be quants. You just need some folks with quant skills to put the right models into your operational systems. This means that the analytical talent you do have is immediately multiplied. Your analytic team build a predictive analytic model, to predict customer churn for example, and that model gets embedded in a decision service that delivers customer retention offers. All your call center representatives now act based on an analytically-enhanced decision without having to have any analytical skills themselves.It was reassuring that

Two-thirds surveyed recognize their decision-making failings and want to fix them

Though it was a pity that they thought more of the same would do so:

nearly three-quarters (72 percent) of the Accenture survey respondents say they are striving to increase their organization's business analytics and BI use.

Someone once said that the definition of insanity was to do the same thing the same way and expect a different result. All these companies have spent a ton of money on BI without changing their decision making. Perhaps they should try something new....

Unless you have a decision or decisions in mind when you start it is all to easy to generate insight without any understanding of how you will make it stick - how it will make a difference. Starting with the decision in mind helps focus the analytic effort on improving that decision. If it is a strategic decision, it focuses the project on helping an executive. If it is an operational one, it means thinking about how the results could be deployed into production.

I would also add something to his point about "General understanding and buy-in of a predictive analytics initiative by stakeholders across business functions". For many predictive analytics projects this needs to include the IT department and the folks working on BI/DW projects and all too often predictive analytics projects include neither.

Note that I am now blogging new posts here on b-eye-network.com and focusing only only BI and analytic topics. You can find my full feed on my personal blog JT on EDM. You can subscribe to the feed too - feeds2.feedburner.com/jtonedm.

Elana Anderson, now at Unica, wrote a nice piece titled Where CRM Goes Next for Baseline Magazine. In a short piece she highlights some of the key challenges for CRM/Marketing going foward:

It must become more focused on interactive marketing, engaging with customers

It must break free from old habits like fixed campaign schedules and a focus only on outbound marketing

It must focus on the multi-channel world that is a reality for most companies today

But, to my mind, the most important point she makes is this one:

Customer decision making will be centralized to drive the dialogue. Imagine a conversation in which one party can't hear or understand what the other is saying. In many respects, this is the kind of conversation most companies have with their customers. The shift from shouting to collaboration and interactive marketing requires marketers to establish a centralized decision-making capability that facilitates dialogue across outbound and inbound, and online and offline marketing channels.

Why do I think this is the most important? Well if you are going to engage with customers across inbound and outbound channels, and across multiple inbound and outbound channels, then you need to do so consistently and analytically. Building this kind of capability into each channel separately not only costs more and makes it harder to leverage analytic insight, it also invites inconsistency. If you are going to replace large, fixed campaigns with something more interactive then you are going to have to automate the decisions about offer, message etc so that they can be delivered quickly and effectively in your new marketing mix - it will no longer be reasonable to sit and plan each one manually like it was for your monthly outbound newsletter or mailing. Without this centralized decision making the rest is simply not possible.

What’s more, a centralized decisioning capability can be used to ensure that marketing is part of every conversation with a customer - support calls, invoicing, statements (transpromotional marketing as it is called) and so on. A centralized decisioning capability also allows non-marketing information to be brought to bear on the problem through a single decisioning hub. This is why Enterprise is the first word of EDM - not because you must do everything enterprise-wide but because you must treat decisions about customers as enterprise decisions. As I like to say:

Customers respond to your decisions like they were deliberate and personal.
Perhaps you should ensure they are.

I have been working with some folks in the NE who have launched a new technology article portal - e-technologymanagement.com.This has a number of focus areas, notably SOA, Agile and Decision Management (in which I am involved, obviously). The portal is just getting started so check it out and subscribe to the feed. You can also submit your own articles using the Submit Article link on the left hand side.

Analysts say that Tesco's big advantage over major international rivals, which also include Germany's Aldi and Lidl, is its unrivaled ability to manage vast reams of data and translate that knowledge into sales. While data crunching may sound dull, it has given Tesco two major advantages: an unmatched ability to operate multiple retail formats--ranging in size from convenience stores to hypermarkets--and the market knowledge to offer what many analysts say is the best and broadest range of house brands from any retailer. (my emphasis)

Tesco uses Dunnhumby, a British data mining firm, and the analytic insight they produce toÂ manage everything from shop formats to store layouts to targeted sales promotions. So why does Tesco scare WalMart? Well because they are the only other retailer who has managed to really put their data to work and this has long been WalMart's big advantage. For years WalMart managed their systems more effectively than anyone, allowing them to stock and replenish products quicker and with less waste. I got a classic illustration of this one summer when it was freakishly hot - everywhere had sold out of fans (because it was hot) except WalMart who had managed to get more delivered, apparently instantly!
But Walmart has not, at least as far as I know, been known as a data mining company. They use their information, sure, but to operate in very well defined bounds. They have not tried to turn that data int insight that would let them try something new. They have also been focused on information about stores not information about customers. This means they lack the kind of customer-centric data that would allow them to target customers individually in marketing, on the web or even in the store. Tesco excels both at finding opportunities for innovaiton in its data mining and in applying that data mining to operational decisions - decision management.
I blogged recently about the growth of analytics in retail and the success of companies like Tesco is only going to push this growth along faster.
If you are interested in what Tesco are doing you might also enjoy reading about Sonetto, the business rules platform they use to support their multi-channel strategy.